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Distribution-free estimation with interval-censored contingent valuation data: troubles with Turnbull?

机译:带有间隔检查的或有估值数据的无分配估算:Turnbull有麻烦吗?

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摘要

Contingent valuation (CV) surveys frequently employ elicitation procedures that return interval-censored data on respondents' willingness to pay (WTP). Almost without exception, CV practitioners have applied Turnbull's self-consistent algorithm to such data in order to obtain nonparametric maximum likelihood (NPML) estimates of the WTP distribution. This paper documents two failings of Turnbull's algorithm; (1) that it may not converge to NPML estimates and (2) that it may be very slow to converge. With regards to (1) we propose starting and stopping criteria for the algorithm that guarantee convergence to the NPML estimates. With regards to (2) we present a variety of alternative estimators and demonstrate, through Monte Carlo simulations, their performance advantages over Turnbull's algorithm.
机译:或然估值(CV)调查经常采用启发程序,该程序返回有关被调查者付款意愿(WTP)的间隔审查数据。几乎没有例外,CV从业人员已将Turnbull的自洽算法应用于此类数据,以获得WTP分布的非参数最大似然(NPML)估计。本文记录了Turnbull算法的两个失败之处。 (1)可能无法收敛到NPML估计,以及(2)可能收敛很慢。关于(1),我们提出了算法的启动和停止标准,以确保收敛到NPML估计。关于(2),我们提出了各种替代估计量,并通过蒙特卡洛模拟证明了它们比Turnbull算法更优越的性能。

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